Submitted by Pedro Henrique Rodrigues (pedro.henriquer@ufpe.br) on 2015-03-04T17:17:29Z No. of bitstreams: 2 dissert-avap.pdf: 401311 bytes, checksum: 5bd3f82323bd612e8265a6ab8a55eda0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-04T17:17:29Z (GMT). No. of bitstreams: 2 dissert-avap.pdf: 401311 bytes, checksum: 5bd3f82323bd612e8265a6ab8a55eda0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2012-03-08 / Cloud reliance is critical to its success. Although fault-tolerance mechanisms are employed by cloud providers, there is always the possibility of failure of infrastructure components. We consequently need to think proactively of how to deal with the occurrence of failures, in an attempt to minimize their effects. In this work, we draw the risk concept from probabilistic risk analysis in order to achieve this. In probabilistic risk analysis, consequence costs are associated to failure events of the target system, and failure probabilities are associated to infrastructural components. The risk is the expected consequence of the whole system. We use the risk concept in order to present representative mathematical models for which computational optimization problems are formulated and solved, in a Cloud Computing environment. In these problems, consequence costs are associated to incoming applications that must be allocated in the Cloud and the risk is either seen as an objective function that must be minimized or as a constraint that should be limited. The proposed problems are solved either by optimal algorithm reductions or by approximation algorithms with provably performance guarantees. Finally, the models and problems are discussed from a more practical point of view, with examples of how to assess risk using these solutions. Also, the solutions are evaluated and results on their performance are established, showing that they can be used in the effective planning of the Cloud.

Transport infrastructure investment decision making is typically based on a range of inputs such as social, environmental and economic factors. The benefit cost ratio (BCR), a measure of economic efficiency (“value for money”) determined through cost benefit analysis (CBA), is dependent on accurate estimates of the various option costs and net social benefits such as reductions in travel time, accidents, and vehicle operating costs. However, most evaluations are deterministic procedures using point estimates for the inputs and producing point estimates for the outputs. Transport planners have primarily focused on the cost risks and treat risk through sensitivity testing. Probabilistic risk analysis techniques are available which could provide more information about the statistical confidence of the economic evaluation outputs. This research project report investigated how risk and uncertainty are dealt with in the literature and guidelines. The treatment of uncertainty in the Nelson Arterial Traffic Study (ATS) was reviewed and an opportunity to apply risk analysis to develop probabilities of sea level rise impacting on the coastal road options was identified. A simplified transport model and economic evaluation case study based on the ATS was developed in Excel to enable the application of @RISK Monte Carlo simulation software. The simplifications mean that the results are not comparable with the ATS. Seven input variables and their likely distributions were defined for simulation based on the literature review. The simulation of seven variables, five worksheets, and 10,000 iterations takes about 30 seconds of computation time. The input variables in rank order of influence on the BCR were capital cost, car mode share, unit vehicle operating cost, basic employment forecast growth rate, and unit value of time cost. The deterministically derived BCR of 0.75 is associated with a 50% chance that the BCR will be less than 0.6, although this probability is partly based on some statistical parameters without an empirical basis. In practice, probability distribution fitting to appropriate datasets should be undertaken to better support probabilistic risk analysis conclusions. Probabilities for different confidence levels can be reported to suit the risk tolerance of the decision makers. It was determined that the risk analysis approach is feasible and can produce useful outputs, given a clear understanding of the data inputs and their associated distributions.

Submitted by Secretaria Pós de Produção (tpp@vm.uff.br) on 2017-07-27T19:21:56Z No. of bitstreams: 1 D2014 - José Cristiano Pereira.pdf: 9830334 bytes, checksum: d5be51799514c74451d0ca3358d7757b (MD5) / Made available in DSpace on 2017-07-27T19:21:56Z (GMT). No. of bitstreams: 1 D2014 - José Cristiano Pereira.pdf: 9830334 bytes, checksum: d5be51799514c74451d0ca3358d7757b (MD5) / O processo de fabricação de motores a jato é complexo. Perigos e riscos e muitos elementos críticos estão presentes em milhares de atividades necessárias para fabricar um motor. Na investigação realizada nota-se a inexistência de um modelo específico para calcular quantitativamente a probabilidade de falha operacional de um motor à jato. O objetivo da tese foi desenvolver um modelo causal para análise de risco probabilística de falhas de motores a jato em situação operacional de fabricação. O modelo se caracteriza pela aplicação de rede Bayesiana associada à árvore de falha / árvore de evento e elicitação de probabilidades por especialistas para quantificar a probabilidade de falha. Para a concepção da construção do modelo, foi inicialmente desenvolvida uma pesquisa bibliométrica, através da consulta aos principais motores de busca nacionais e internacionais, em periódicos científicos e técnicos, bancos de dissertações/teses e eventos técnicos relacionados ao tema, para estabelecimento dos estado-da-arte e da técnica. Para a estimativa das probabilidades associadas aos cenários de falhas propostos, foi desenvolvido um processo de elicitação de probabilidade a partir da consulta a especialistas e técnicos. Na concepção do modelo foram consideradas três áreas de influência para a confiabilidade do sistema: humana, software e calibração. Como resultado foi desenvolvido o modelo CAPEMO, que é suportado por um aplicativo que utiliza a teoria das probabilidades (Lei de Bayes) para modelar incerteza. A probabilidade de falha estimada ao final da processo de fabricação, antes do motor ser colocado em operação, contribui no processo de tomada de decisão, melhoria da segurança do sistema e redução de riscos de falha do motor em operação / The process of jet engines manufacturing is complex. Hazards and risks and many critical elements are present in the thousands of activities required to manufacture an engine. In the conducted investigation it is observed a lack of a specific model to estimate quantitatively the probability of a jet engine operational failure. The goal of this thesis is to develop a causal model for probabilistic risk analysis of jet engines failure in manufacturing situational operation. The model is characterized by the application of Bayesian Network associated with the fault tree and event tree to quantify the probability of failure. For the establishment of state-of-the-art and technique and for the conception and construction of the model, a bibliometric research was conducted in the main national and international search engines, in the scientific and technical journals, in the database of dissertations/theses and technical events related to the topic. For the estimation of the probabilities associated with the proposed fault scenarios, a process of probability elicitation from technicians and experts was developed. In the design of the model three areas of influence for the reliability of the system were considered: human, software and calibration. As a result CAPEMO model was developed, that is supported by a software application that uses probability theory to model uncertainty. The probability of engine failure estimated at the end of the manufacturing process, before the motor be put into operation, helps in the allocation of resources in the decision-making process and improves system safety reducing the risk of engine failure in operation